# A Review of Intelligent Fault Diagnosis for High-Speed Trains: Qualitative Approaches

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## Abstract

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## 1. Introduction

- Gradation. The structure of high-speed trains has several levels, including train level, system level, subsystem level, and component level. Thus, referring to gradational structure of trains, their faults and symptoms have similar features [21].
- Confusion. Aiming at complex system with the high structure coupling, the relationship among different fault characteristics is complicated. When fault occurs, factors, like redundancy and relevance, reflecting on these characteristics should be considered [22].
- Propagation. When a fault appears in systems, it is a high probability with the phenomenon of causing other systems or subsystems to fail at the same time [23].
- Uncertainty. The occurrence of faults in high-speed trains is often random. Moreover, there are still several uncertainties under the following situations, such as monitoring process of measurement data, transformation in external operation environment, and so on [24].

- (1)
- The first aim of this paper is to deliver a report about the detailed structure of high-speed trains (including their common fault types and core systems) and then to provide researcher with the diagnostic objective suitable for the FD tasks of high-speed trains.
- (2)
- The second focus of this paper is to detail the extraction path of diagnostic knowledge, where extraction rules are significant for building the high-performing knowledge base (KB). It is preparation work for using qualitative IFD methods to achieve FD proposes for high-speed trains.
- (3)
- The third attempt of this paper is, by reviewing the research work of qualitative techniques and surveying the emerging techniques, to summarize qualitative IFD techniques from new perspectives.
- (4)
- The final attention of this paper is, by enumerating the limitations and future investigations of qualitative IFD techniques, to inspire new research ideas for researchers and engineers.

## 2. Background

#### 2.1. High-Speed Trains

**Car body:**it is the main place for passengers and drivers to stay. Another use is the basic system for installing and connecting other equipment.

**The connection between car parts:**its core function is to connect various types of equipment which can usually affect the normal operation of trains, such as electrical or air pipeline connection between multi-section carriages, the connection between control systems and other systems, etc.

**Exterior door and interior facility:**the door is divided into the outer door and inner door according to the function and installation position. Specifically, outer doors are further divided into sliding plug doors and built-in side sliding doors. In addition, inner doors are the access doors connecting the carriages.

**Driving room:**it is the place for the drivers to control the high-speed train. According to its function, drivers can obtain the train operation information, send operation signals, and achieve various operations at the driver’s cab.

**Bogie:**it is generally divided into power bogie and non-power bogie. In reality, the bogie system is also known as a running gear system. Besides this, it is useful to pull and guide the train along the track through the electro-pneumatic braking system. Please note that it is a core system to ensure the quality and safety of train operations.

**Mains power supply:**it is the electrical equipment used in main circuits, the so-called high-voltage equipment, mainly including pantograph, vacuum break, high-voltage disconnector, and etc., transmitting the electric energy among the catenary to the electrical system.

**Traction:**it is further divided into the direct-current (DC) drive system and the alternating current (AC) drive system. Because the subsystem responsible for the control function is mainly the traction motor in traction system, it is convenient for high-speed trains to realize traction and braking functions by adjusting the torque and speed of the motors.

**Auxiliary electric:**it is generally composed of auxiliary winding of traction transformer, auxiliary converter, power socket, storage battery, and charger. Its function is to provide power for point-type equipment except for the traction power system of high-speed trains.

**Air supply braking:**its function is realized by the hybrid braking which is composed of power braking and air braking. Air supply braking system is necessary to ensure the safe operation of trains.

**Network and auxiliary monitoring:**it is generally composed of input-output network equipment and each subsystem controller and then to achieve the data transmission, sharing information, real-time control, and FD task through train control network.

**Air-conditioner:**it is generally composed of the ventilation system, refrigeration system, heating system, and operation control system. Its principal purpose is to regulate the temperature and humidity in the car through automatic control. Benefiting from its strong function, air-conditioner system can filter the circulating air, providing and maintaining a desirable internal atmospheric environment for passengers without the negative influence caused by external conditions.

#### 2.2. The Classification and Expression about Diagnostic Knowledge in High-Speed Trains

- Easy to be understood and implemented. Each means of expression should be consistent with the human logical thinking so that it can be easily described by the computer.
- Easy to use knowledge for reasoning. The objective of knowledge expression and knowledge storage is used for knowledge for reasoning, and further analyzing the fault causes. If the structure of knowledge expression is complex or the knowledge is difficult to understand, it will reduce the diagnostic efficiency.
- Beneficial to the management of KB. During the operation of the system, some knowledge will be increased or reduced according to the actual situation. Therefore, the selection of appropriate knowledge expressions could reduce the difficulty of knowledge management and improve the efficiency of knowledge maintenance.

#### 2.3. Advantages of Qualitative IFD Methods

- It is easy to be designed and used.
- It is especially suitable for FD tasks with sufficient data.
- It can be implemented without accurate parameters.
- It can provide the basis for the diagnostic results (because a strong causal relationship exists among various fault modes and fault causes).
- It can make clear and transparent reasoning in uncertain situations.

## 3. Applications of Qualitative IFD Approach in High-Speed Trains

#### 3.1. Symbol and Logical Reasoning in Qualitative IFD Approach of High-Speed Trains

#### 3.1.1. Rule-Based Reasoning

#### 3.1.2. Case-Based Reasoning

#### 3.1.3. Expert System

#### 3.2. Graph Theory in Qualitative IFD Approach of High-Speed Trains

#### 3.2.1. Directed Graph

#### 3.2.2. Undirected Graph

#### 3.2.3. Clain Graph

- (1)
- If $\left\{X,Y\right\}$ is an edge in G, and $X,Y\in {B}_{i}$, then the form of an edge will be $X-Y$.
- (2)
- If $\left\{X,Y\right\}$ is an edge in G, and $X\in {B}_{i},Y\in {B}_{j},i<j$, then the form of an edge will be $X\to Y$.

#### 3.2.4. Fault Tree Analysis

- (1)
- Fault types ought to be as extensive as possible.
- (2)
- The analysis of fault events should be as detailed as possible.
- (3)
- The principle of layer by layer transmission should be followed (please pay attention to the function of the gates in FTA methods).
- (4)
- Only gates in FTA approaches can be connected to events.

#### 3.3. Fuzzy Theory in Qualitative IFD Approach of High-Speed Trains

- Step 1: Extract the fault characteristic fuzzy vector $X=({\mu}_{x1},\dots ,{\mu}_{xn})$ and fault cause fuzzy vector $Y=({\mu}_{y1},\dots ,{\mu}_{ym})$. There are n characteristics generated by faults of high-speed trains, in which the domain is $U=({x}_{1},\dots ,{x}_{n})$. The variable describing the i-th characteristic is ${u}_{i}$, and its membership function is ${\mu}_{ui}$. On this basis, $X=({\mu}_{x1},\dots ,{\mu}_{xn})$ is established as the fault characteristic fuzzy vector. Besides, there may be m fault causes when faults of systems occur, its domain is $V=({y}_{1},\dots ,{y}_{m})$. The variable describing the j-th cause is ${v}_{j}$, and its membership function is ${\mu}_{yj}$. On this basis, $Y=({\mu}_{y1},\dots ,{\mu}_{ym})$ is established as the fault cause fuzzy vector.
- Step 2: Calculate the diagnostic matrix R. There is a connection among many facts in FD procedures, so the relationship among faults and fault symptoms is established through the diagnostic relation matrix R from U to V. In addition, the above relationship can be represented by an ordered pair $(x,y)$, where the Cartesian product set of U domain and V domain is $U\times V=\left\{(x,y)|x\in X,y\in Y\right\}$. Furthermore, R is a fuzzy set defined on $U\times V$, as follows:$$R=\left[\begin{array}{cccc}{r}_{11}& {r}_{12}& \cdots \phantom{\rule{4pt}{0ex}}& {r}_{1n}\\ {r}_{21}& {r}_{22}& \cdots \phantom{\rule{4pt}{0ex}}& {r}_{2n}\\ \vdots \phantom{\rule{4pt}{0ex}}& \vdots \phantom{\rule{4pt}{0ex}}& \ddots \phantom{\rule{4pt}{0ex}}& \vdots \phantom{\rule{4pt}{0ex}}\\ {r}_{m1}& {r}_{m2}& \cdots \phantom{\rule{4pt}{0ex}}& {r}_{mn}\end{array}\right],$$
- Step 3: Implement the fuzzy reasoning. Suppose R is a fuzzy relation from U to V. By means of fuzzy relation $R(x,y)=U\to V=U\times V$, the fuzzy set ${V}^{{}^{\prime}}$ on V can be calculated. With the help of characteristic fuzzy vectors ${U}^{{}^{\prime}}$, the new domain of fault fuzzy vectors can be calculated via:$$\begin{array}{c}\hfill {V}^{{}^{\prime}}={U}^{{}^{\prime}}\circ (U\to V)={V}^{{}^{\prime}}\circ R,\end{array}$$
- Step 4: Perform the fuzzy FD. Faults can be matched with their causes by the maximum membership criterion. Specifically, if the element ${\mu}^{*}$ belongs to U domain and satisfies ${\mu}_{c}\left({\mu}^{*}\right)=max[{\mu}_{c}\left({\mu}_{1}\right),\dots ,{\mu}_{c}\left({\mu}_{n}\right)]$, then the fault characteristic ${X}_{i}$ will be matched with the fault cause ${Y}_{j}$.

## 4. Challenges and Future Trends

- Qualitative IFD techniques are useful for a specific system.
- In qualitative IFD techniques, it is difficult to ensure that all rules are applicable.
- The lower quality of knowledge results in worse FD performance in qualitative IFD techniques.
- With the complexity of system mechanism, knowledge becomes difficult to be extracted and stored.
- Qualitative IFD techniques are difficult to diagnose and detect incipient faults in high-speed trains.
- The diagnostic KB with complete fault knowledge, viewed as a prerequisite for using qualitative IFD techniques, is difficult to be constructed.

- (1)
- Management and maintenance of explicit diagnostic knowledge. It is well known that the construction of diagnostic KB about high-speed trains is a huge task. One of the difficulties lies in the need to expose invisible knowledge because most researchers or engineers only use existing technologies and explicit diagnostic knowledge to build a KB containing enough rich information. But, it is not even close to sufficient. Explicit knowledge usually contains the two types of known information discussed in Section 2.2. But, invisible knowledge, especially in the human brain of train maintenance engineers, is also an indispensable knowledge resource. At the moment, there have been many studies aiming at explicit knowledge extraction, but few reports consider invisible knowledge extraction. To overcome this difficulty, it is helpful to construct a complete diagnostic KB from the perspective of knowledge extraction.
- (2)
- Improvements in the quality of known quantitative information in high-speed trains. Based on the analysis in Section 2, a large amount of historical data is recorded during the operation of high-speed trains and then can be converted into fault knowledge through data mining methods. However, these data collected from the onboard information system in high-speed trains often suffer from missing data points. When extracting knowledge from missing data and building a diagnostic KB, it is easy to lose important knowledge. Thus, the selection of appropriate preprocessing techniques can improve the quality of knowledge discovery and monitoring data (will be used in data mining methods to extract fault knowledge), thereby improving the quality of the diagnostic KB and the result provided via qualitative IFD techniques.
- (3)
- Deep knowledge mining, extraction, and application. Shallow knowledge could be summarized from the massive historical data collected from high-speed trains. On the contrary, deep knowledge is helpful to explore relationships among subsystems in high-speed trains, providing the new solution for system level faults. It is expected that qualitative IFD techniques combining deep and shallow knowledge will be further developed in the future, so as to break the constraints of traditional qualitative methods for special applications in system level or component level FD.
- (4)
- The fusion of qualitative IFD and health management approaches. Under some special conditions (e.g., complete diagnostic KB, the transparent and interpretable FD procedure), qualitative IFD approaches can show accuracy results. These conditions are also necessary for health management techniques. One emerging solution for qualitative IFD approaches is to integrate into health management techniques, and the whole framework can be regarded as an autonomous and accurate comprehensive evaluation system for high-speed trains. With the critical advantages of health management, engineers can easily report the dynamic degradation of high-speed trains, providing effective suggestions for train maintenance. However, there are some challenges with the above technology, like system integration, sensor selection and optimal layout, and measurement data fusion. Fortunately, solutions to these challenges can improve the reliability of high-speed trains and reduce the operation cost of systems.
- (5)
- The research and application of integrated qualitative IFD techniques. Some critical systems in high-speed trains usually have complex nonlinear features, such as strong coupling and time-varying parameters. In addition, process uncertainties and external interferences also have negative effects on FD procedures. Therefore, different qualitative IFD approaches need to be integrated to improve the FD effect. However, there are still many problems to be further studied, such as combination principles of different methods, the fuzzy knowledge expression after fusion, etc.
- (6)
- The research and application of distributed qualitative IFD techniques. With the development of materials and technologies, high-speed trains are becoming systematic, continuous, and automated, many distributed frameworks, like distributed open-scale FD systems, are applied in FD procedures of trains. Distributed techniques provide a potential way for large-scale IFD. Through the description, decomposition and allocation of FD tasks, distributed qualitative IFD techniques can be designed for the decentralized and problem-oriented subsystems to overcome challenges in a parallel collaboration. Furthermore, FD schemes based on the fusion of multi-agent techniques and qualitative IFD techniques are also the advanced research topics in FD domains.
- (7)
- The research and application of remote cooperative qualitative IFD techniques. The premise is to integrate computer networks into qualitative IFD techniques, in which multicenter computers as servers work together. With the aid of computer remote monitoring, information transmission, remote IFD techniques are easy to realize the processing, transmission, storage, query, and display of monitoring information in high-speed trains. The successful implementation of remote cooperative qualitative IFD techniques will be helpful for online IFD in high-speed trains, providing real-time results for engineers in the operation center. Based on these results, engineers and experts can adjust maintenance plans of trains. The key to this technique includes remote signal analysis, remote transmission of real-time data, and open ES design.

## 5. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

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**Figure 3.**Diagram for key systems suitable as the diagnostic objective (supplement to Figure 2).

**Table 1.**All reviews in the field of diagnosis from 1990 to present according to the theme and application of each research.

Method Classification | Reference | Application Scenarios |
---|---|---|

Quantitative technique [35] | ||

Model-based | Cheng [36], Zeng [37], Xu [38], Yang [39], Xue [40], Lei [41], Ma [42], Dai [43], Shui [44], Wang [45], Mellit [46], Liu [47] | Permanent magnet machine [36], electronic systems [39,40,48], planetary gearbox [41], nuclear power plants [42], power generation equipment [49], dynamic systems [50], aerospace systems [51], wind turbine [52] |

Data driven | Chen [1], Ma [42], Du [49], Huang [53], Yang [54], Henao [55], Niu [56], Norazwan [57], Dai [58] | Traction system [1], nuclear power plants [42], power generation equipment [49], converter [53], aeroengine [54], rotating electrical machines [55], multi-axle speed sensors [56], chemical process systems [57], industrial automation [58] |

Signal-based | Chen [36], Zeng [37], Xu [38], Yang [39], Xue [40], Lei [41], Ma [42], Dai [43], Shui [44], Wang [45], Mellit [46], Liu [47] | Permanent magnet machine [36], wind turbines [37,38], electric systems [39,40], planetary gearbox [41], nuclear power plants [42], control systems [44], hydraulic systems [43], rotating machines [45], photovoltalic systems [46], squirrel-cage induction motors [47] |

Neural Network | Liu [14,59], Yan [60], Wang [61,62], Wen [63], Amiruddin [64], Fenton [48], Hu [65], Li [66,67], Chen [68], Xie [69] | Rotating machinery [14,59], servo system [60], turbine [63], engineering-related systems [64], electric systems [48], rolling bearing [65], diesel engine [66], motor engine [68], gas turbine [69], running gear system [67] |

Machine Learning | Liu [14], Zhang [61], Duan [70], Saufi [62], Hu [66], Li [67] | Rolling bearing [14,65,71], high-speed railway [67] |

Artificial Intelligence | Mellit [46], Liu [47], Nandi [72], Wen [63] | Photovoltalic systems [46], squirrel-cage induction motors [47], electrical motors [72], turbine [63] |

Petri Nets | Zaytoon [73], Niu [56] | Discrete event systems [73], multi-axle speed sensors [56] |

Qualitative technique [35] | ||

Expert System | Yan [60], Wang [61], Li [66], Chen [68], Xie [69], Lin [59] | Servo system [60], motor engine [68], gas turbine [69], rotating machinery [59], high-speed railway [67] |

Fuzzy Theory | Wang [61,74], Li [66], Chen [68], Xie [69], Lin [59] | Diesel engine [66], motor engine [68], rotating machinery [59] |

Knowledge-based | Cheng [36], Yang [39], Xue [40], Du [49], Wang [75] | Permanent magnet machine [36], electric systems [39,40,75], power generation equipment [49] |

Rule and Case-based | Wang [47], Fenton [48] | Electrical systems [48] |

Fault Tree | Wang [61], Zaytoon [73], Chen [68] | Discrete event systems [73], motor engine [68] |

Method | Advantage | Limitation |
---|---|---|

RBR | 1. Easy-to-understand forms of reasoning 2. Expression of uncertain knowledge 3. Easy interpretation | 1. Hard to grasp the overall structure of knowledge 2. Unclear relationships among the rules 3. Lack of flexibility in reasoning |

CBR | 1. The extended coverage of case bases with the continuous use of systems 2. No rule extraction | 1. A complicated knowledge extraction process 2. The low retrieval efficiency in large case base 3. Consistency test of difficult case correction |

ES | 1. Transparent and interpretable FD procedures 2. No requirements of mathematical equations 3. Easy to determine the fault cause | 1. Lack of the self-learning and self-adaptive ability 2. The slow reasoning speed and low efficiency 3. Not ideal for the real-time performance |

DG | 1. Processing of various uncertainties via graphs 2. More forms of knowledge expressions 3. No need for other reasoning methods | 1. Easy to lose vital variables in fault propagations 2. Dependent on the experience and simulation 3. Further simplification of the model |

UG | 1. The expression from global graphic forms | 1. Difficult to accurately locate faults |

CG | 1. No requirements of mathematical equations 2. Simple and easy to be operated | 1. Complex search procedure 2. Easy to lose information |

FTA | 1. Good logic performance 2. Easy modeling | 1. High requirements for failure mechanism 2. Weak real-time processing ability |

Fuzzy Theory | 1. More solutions with different priorities 2. The ability to analyze uncertainty issues | 1. With subjective factors 2. No self-learning ability |

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Cheng, C.; Wang, J.; Chen, H.; Chen, Z.; Luo, H.; Xie, P. A Review of Intelligent Fault Diagnosis for High-Speed Trains: Qualitative Approaches. *Entropy* **2021**, *23*, 1.
https://doi.org/10.3390/e23010001

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Cheng C, Wang J, Chen H, Chen Z, Luo H, Xie P. A Review of Intelligent Fault Diagnosis for High-Speed Trains: Qualitative Approaches. *Entropy*. 2021; 23(1):1.
https://doi.org/10.3390/e23010001

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Cheng, Chao, Jiuhe Wang, Hongtian Chen, Zhiwen Chen, Hao Luo, and Pu Xie. 2021. "A Review of Intelligent Fault Diagnosis for High-Speed Trains: Qualitative Approaches" *Entropy* 23, no. 1: 1.
https://doi.org/10.3390/e23010001